Simulation Study of Discrete Event Systems using Fast Approximation Method of Single Run and Optimization Method of Multiple Run

단일 실행의 빠른 근사해 기법과 반복 실행의 최적화 기법을 이용한 이산형 시스템의 시뮬레이션 연구

  • Park, Kyoung Jong (Department of Business Administration, Gwangju University) ;
  • Lee, Young Hae (Department of Industrial Engineering, Hanyang University)
  • Published : 2006.03.31

Abstract

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event simulation. The developed algorithm uses the configuration algorithm that can change decision variables and the stopping algorithm that can end simulation in order to satisfy the given objective value during single run. It tries to estimate an auto-regressive model for evaluating correctly the objective function obtained by a small amount of output data. We apply the proposed algorithm to M/M/s model, (s, S) inventory model, and known-function problem. The proposed algorithm can't always guarantee the optimal solution but the method gives an approximate feasible solution in a relatively short time period. We, therefore, show the proposed algorithm can be used as an initial feasible solution of existing optimization methods that need multiple simulation run to search an optimal solution.

Keywords

References

  1. Ahmed, M. A., T. M. Alkhamis, and M. Hasan (1997), Optimizing discrete stochastic systems using simulated annealing and simulation, Computers & Industrial Engineering, 32(4), 823-836 https://doi.org/10.1016/S0360-8352(97)00006-5
  2. Andradottir, S. (1996), A global search method for discrete stochastic optimization, SIAM Journalon Optimization, 6(2), 513-530 https://doi.org/10.1137/0806027
  3. Andradottir, S. (1995), A method for discrete stochastic optimization, Management Science, 41(12), 1946-1961 https://doi.org/10.1287/mnsc.41.12.1946
  4. Andradottir, S. (1992), Discrete optimization in simulation: a method and applications, Proceedings of the 1992 Winter Simulation Conference, 483-486
  5. Azadivar, F. (1992), A tutorial on simulation optimization, Proceedings of 1992 Winter Simulation Conference, 198-204
  6. Azadivar, F. and Y. H. Lee (1988), Optimization of discrete variable stochastic systems by computer simulation, Journal of Mathematics & Computers in Simulation, 30, 331-345 https://doi.org/10.1016/S0378-4754(98)90004-0
  7. Barry, D. A. and B. Fristedt (1985), Bandit problems, Chapman and Hall, London
  8. Chen, H. (1994), Stochastic root finding in system design, working paper SMS 94-8, School of Industrial Engineering, Purdue University, U.S.A.
  9. Fu, M. C. and K. J. Healy (1997), Techniques for optimization via simulation: an experimental study on (s,S) inventory system, IIE Transactions, 29(3), 191-200
  10. Gong, W. B., Y. C. Ho, and W. Zhai (1992), Stochastic comparison algorithm for discrete optimization with estimation, Proceedings of the 3IstIEEE Conference on Decision and Control, 795-800
  11. Hillier, F. S. and G. J. Lieberman (1990), Introduction to Operations Research, 5th ed. McGraw-Hill
  12. Law, A. M. and W. D. Kelton (1995), Simulation Modeling and Analysis, McGraw-Hill
  13. Lee, Y. H. and K. Iwata (1991), Part ordering through simulation-optimization in a FMS, International Journal of Production Research, 29(7), 1309-1323 https://doi.org/10.1080/00207549108948012
  14. Park, K. J. and Lee, Y. H. (1999), A Method for Design of Discrete Variable Stochastic Systems using Simulation, Journal of the Korea Society for Simulation, 8(3), 1-16
  15. Pierreval, H. and L. Tautou (1997), Using evolutionary algorithms and simulation for the optimization of manufacturing systems, IIE Transactions, 29, 181-189
  16. Shi, L. and O. Sigurdur (1997), Nested partitions method for stochastic optimization, Technical Report, Dept. of I. E., University of Wisconsin-Madison
  17. Wild, R. H. and J. J. Pignatiello (1994), Finding stable system designs: a reverse simulation technique, Communications of the ACM, 35(10), 87-98
  18. Yakowitz, S. and E. Lugosi (1990), Random search in the presence of noise with application to machine learning, SIAM Journalon Scientific Statistical Computing, 11, 702-712 https://doi.org/10.1137/0911041
  19. Yan, D. and H. Mukai (1992), Stochastic discrete optimization, SIAM Journal on Control and Optimization, 30, 594-612 https://doi.org/10.1137/0330034